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dc.creatorDelamare-Deboutteville, J.en_US
dc.creatorBarnes, A.en_US
dc.creatorWilkinson, S.en_US
dc.creatorChadag, V.en_US
dc.creatorHuso, D.en_US
dc.date.accessioned2020-08-30T08:08:43Z
dc.date.available2020-08-30T08:08:43Z
dc.date.issued2020
dc.identifier.citationJerome Delamare-Deboutteville, Andrew Barnes (Producer), Shaun Wilkinson (Producer), Vishnumurthy Mohan Chadag (Producer), Doina Huso (Producer). (7/5/2020). A New Concept for Rapid Genomic Detection of Fish Disease. Bayan Lepas, Malaysia: WorldFish (WF) (Executive Producer). https://www.youtube.com/watch?v=iFxbodO6Fosen_US
dc.identifier.urihttps://hdl.handle.net/20.500.12348/4323
dc.description.abstractIn 2019, a WorldFish team, led by Dr. Jerome Delamare-Deboutteville, received a CGIAR Inspire Challenge award worth USD $100,000 to test a new concept for rapid detection and diagnosis of fish disease. The project's cross-functional research team includes experts from the University of Queensland, Australia, Wilderlab, New Zealand, SAF/CEFAS center at the University of Exeter, United Kingdom, GeneSEQ, Malaysia, and Centex at University of Mahidol, Thailand. The proposed approach brings together years of fish pathogen data and combines it with new pathogen DNA sequences from infected fish. Processed using specially-designed software and machine learning tools, the data will feed a cloud-based service that can be used to accurately detect and diagnose a range of fish diseases. Critical information on how to manage the disease will then be sent back to farmers, extension workers, hatcheries, and quarantine officers in near-real time, in a user-friendly format via a smartphone. The team expects the approach will reduce the need for antibiotic treatments and ensure the right treatments are used where necessary; reduce the risk of the subsequent production cycles of fish being infected and prevent the spread of disease between farms. In addition, the system will provide scientists with useful information for vaccine development. If successful, the approach also could be used to rapidly and accurately detect and manage diseases in all farmed animals.en_US
dc.formatMP4en_US
dc.languageenen_US
dc.publisherWorldFish (WF)en_US
dc.rightsCC-BY-NC-ND-4.0en_US
dc.subjectgenomic toolen_US
dc.subjectbacterial genomicsen_US
dc.subjectoxford nanopore technologiesen_US
dc.subjectrapid diagnoticsen_US
dc.subjectFishen_US
dc.titleA New Concept for Rapid Genomic Detection of Fish Diseaseen_US
dc.typeVideoen_US
cg.contributor.crpFISHen_US
cg.contributor.funderCGIAR System Officeen_US
cg.coverage.countryAustraliaen_US
cg.coverage.countryBangladeshen_US
cg.coverage.countryMalaysiaen_US
cg.coverage.countryNew Zealanden_US
cg.coverage.countryThailanden_US
cg.coverage.regionAustralia and New Zealanden_US
cg.coverage.regionSouthern Asiaen_US
cg.coverage.regionSouth-Eastern Asiaen_US
cg.identifier.worldfish4843
cg.subject.agrovocaquacultureen_US
cg.subject.agrovocai (artificial intelligence)en_US
cg.subject.agrovocmachine learningen_US
cg.subject.agrovocfish diseasesen_US
cg.contributor.affiliationWorldFishen_US
cg.contributor.affiliationUniversity of Exeteren_US
cg.contributor.affiliationThe University of Queenslanden_US
cg.contributor.affiliationWilderlab NZ Ltden_US
cg.contributor.affiliationCentre for Environment, Fisheries and Aquaculture Scienceen_US
cg.contributor.affiliationMahidol University, Faculty of science, Center of Excellence for Shrimp Molecular Biology and Biotechnologyen_US
cg.contributor.affiliationIndependent / Not associateden_US
cg.identifier.statusOpen accessen_US
cg.contribution.worldfishauthorDelamare-Deboutteville, J.en_US
cg.contribution.worldfishauthorChadag, V.en_US
cg.contribution.worldfishauthorHuso, D.en_US
cg.description.themeSustainable aquacultureen_US
cg.identifier.urlhttps://www.youtube.com/watch?v=iFxbodO6Fosen_US
cg.creator.idJerome Delamare-Deboutteville: 0000-0003-4169-2456en_US
cg.creator.idVishnumurthy Mohan Chadag: 0000-0002-2574-284Xen_US
cg.creator.idDoina Huso: 0000-0002-4477-2593en_US


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